0
  • DE
  • EN
  • FR
  • Base de données et galerie internationale d'ouvrages d'art et du génie civil

Publicité

Deep learning‐based classification and instance segmentation of leakage‐area and scaling images of shield tunnel linings

Auteur(s): ORCID (Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education and Department of Geotechnical Engineering Tongji University Shanghai China)
(Department of Civil Engineering Sharif University of Technology Tehran Iran)
ORCID (Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education and Department of Geotechnical Engineering Tongji University Shanghai China)
(Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education and Department of Geotechnical Engineering Tongji University Shanghai China)
(Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education and Department of Geotechnical Engineering Tongji University Shanghai China)
Médium: article de revue
Langue(s): anglais
Publié dans: Structural Control and Health Monitoring, , n. 6, v. 28
DOI: 10.1002/stc.2732
Structurae ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.1002/stc.2732.
  • Informations
    sur cette fiche
  • Reference-ID
    10601173
  • Publié(e) le:
    17.04.2021
  • Modifié(e) le:
    08.05.2021
 
Structurae coopère avec
International Association for Bridge and Structural Engineering (IABSE)
e-mosty Magazine
e-BrIM Magazine